University of Ibadan Journals
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Artificial Intelligence-Assisted Retrieval of Owo Cultural Artifacts from Heterogeneous Online Sources
Full text linkPreservation of cultural heritage has increasingly been dependent on artificial intelligence (AI) to tackle issues of documentation, accessibility and retrieval. The present research centers around the system of an AI-based retrieval system designed for Owo cultural objects, a historically important collection in Ondo State, Nigeria. Through machine learning, natural language processing, and chatbot technology, the system overcomes access barriers, improves familiarity with, and comprehension of these artifacts. The study is based on data collection across a range of sources, data pre-processing steps to enable structured storage, and the development of a Flask based API to provide a platform for easy and on demand retrieval. A chatbot driven by Botpress is used as the user interface to allow the system to be used via natural language queries. The AI model, by learning textual and image-based representation, showed excellent accuracy for artifact retrieval, and multimodal learning itself further improved performance of classification. The paper demonstrates the possibility of application of AI as connecting bridge between classical preservation techniques and current digital accessibility, guaranteeing the permanent recording and interaction with Owo cultural properties. Future developments include augmenting NLP functionality, scaling the system, and increasing the scope of the datasets in order to further improve accuracy of artifact retrieval
REJUVINATING TEACHER EDUCATION THROUGH MANAGEMENT FUNCTIONS FOR SUSTAINABLE DEVELOPMENT IN NIGERIA
No full textThe teacher plays a vital role in the success of all classroom educational interventions that contribute to national development. The Federal Republic of Nigeria (FRN, 2004:39) emphasised that the quality of school teachers is crucial for the success of any education system. Therefore, teacher education should remain a primary focus in all educational planning and development. Therefore, it is an established reality that all teachers must be committed to fulfilling their responsibilities adequately. In order to revitalise teacher education for sustainable development, it is necessary to alter people's perspective on teachers and the teaching profession. The government and the public should acknowledge the profession to attract young individuals to it. Additionally, there is a need to enhance the quality of infrastructural facilities in teaching institutions. The outcome is heavily contingent upon the amount of financial resources allocated to the system. Sustainable national development refers to the process of achieving development that fulfils the current requirements without jeopardising the abilities of future generations to fulfil their own demands. This entails incrementally and judiciously advancing in life. The function of education in sustainable national development is to instruct and prepare citizens to live responsibly and consider the well-being of society and future generations in their pursuits. This involves including pressing sustainable development concerns such as climate change, disaster risk reduction, biodiversity, poverty reduction, and sustainable consumption into educational curricula. Only by preventing environmental degradation, ensuring equitable distribution of national prosperity, and prioritising the development of human resources can any country attain sustainable economic growth and development. Application of participatory teaching and learning approaches that inspire and enable learners to modify their behaviours and engage in actions leads to sustainable development. Only by providing sufficient teacher training can this be accomplished. This research thus suggests the need of increasing awareness through mobilisation and advocacy, as a significant number of interested learners lack knowledge about the presence of teacher education courses and the specific programmes they are required to participate in
Modeling and Forecasting of Financial Time Series in Emerging Markets using Multilayer Perceptrons
Full text linkThis study develops a data-driven forecasting framework for the Nigerian Stock Exchange All Share Index (NSE ASI) using a Multilayer Perceptron (MLP) neural network. Financial markets, particularly in emerging economies, are characterized by volatility, regime shifts, and nonlinear dependencies that limit the effectiveness of traditional statistical models. To address these challenges, this work applies a deep learning pipeline incorporating rigorous data preprocessing, feature scaling, and supervised learning for univariate time series prediction. The model is trained on daily NSE ASI data and evaluated using standard metrics such as MSE, RMSE, MAPE, and R². Diagnostic analysis includes autocorrelation structure, outlier detection, and SHAP based interpretability to assess feature influence and market anomalies. The MLP model demonstrates strong predictive performance across both stable and turbulent regimes, notably capturing post-COVID market momentum. The results affirm the suitability of neural networks in modeling financial indices in emerging markets and highlight the value of integrating explainable AI into financial forecasting systems
A Machine Learning Framework for Classifying Haemoglobin Levels in Sickle Cell Anaemia Patients
Full text linkSickle Cell Anaemia (SCA) significantly impacts haemoglobin (HGB) levels, leading to severe health complications with high mortality rates. In Nigeria, about 2% of newborns, approximately 150,000 annually, are diagnosed with SCA. Accurate HGB monitoring is essential for effective disease management, yet traditional methods are labour-intensive and prone to errors. This necessitates automated and reliable diagnostic techniques like machine learning (ML) for improved SCA management. This study classifies HGB levels in SCA patients using clinical records and ML techniques. A dataset of 364 records (203 female population) was obtained from Kaggle; a public data repository containing eleven (11) features namely: age, sex, red blood cell (RBC) count, packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red cell distribution width (RDW), total leukocyte count (TLC), platelets per cubic millimeter (PLT/mm³), and haemoglobin (HGB). Two ML models, Logistic Regression (LR) and Support Vector Machine (SVM), were used with two feature selection methods: all features and selected features. The latter identified age, RBC, PCV, MCV, and HGB as key predictors. Continuous HGB values were categorized into (1) low, (2) normal, and (3) high using standard medical metrics. SMOTE analysis was also carried out to mitigate class imbalance. SVM with a Radial Basis Function (RBF) kernel achieved 84.90% accuracy and AUC-ROC of 93.40%, while LR underperformed with 79.50% accuracy and AUC-ROC of 90.90%. Using all feature selection, SVM improved to 91.80% accuracy and AUC-ROC of 98.20%, with LR achieving accuracy of 93.20% and AUC-ROC of 98.90%. Both models demonstrated high accuracy, with LR excelling using all features, while SVM performed better with selected features. Future work will involve the use of primary datasets, additional feature selection techniques and ML algorithms, and incorporate the use of Haemoglobin variants to provide further insight into SCA progression and in turn offer personalized treatment.  
Igbo Text Named Identity Recognition (NER) System using Natural Language Processing Algorithms: A Review
Full text linkThis is a review paper, which is concerned with the recent nature of Named Entity Recognition (NER) for the Igbo language. It is a low-resource language spoken in the Southeastern part of Nigeria. Irrespective of the numerous advancements in NER for high-resource languages, Igbo NER so far remains underrepresented. This is for its unique linguistic challenges, which includes morphological richness and dialect variations. In recent times, frank efforts have been put forward by MasakhaNER and WAZOBIA NER projects to develop NER datasets and models for the Igbo language. The existing datasets are limited in size and domain coverage. For this reason there are needs for high-quality, large-scale, manually annotated NER datasets for real-world deployment. This paper reviews the existing literature works on Igbo NER, highlighting the challenges, creating opportunities and looking into the potential applications of NER in developing Igbo digital assistants, intelligent search, and machine translation. This work aims to contribute to the growth and development of low-resource African NLP with the provision of future research in indigenous language NE
Utilizing Convolution Neural Network (CNN) Algorithm for the Classification of Visual, Auditory, Read/Write, and Kinesthetic (VARK) Learning Styles Based On Real-Time Datasets
Full text linkIdentifying learners’ preferred learning styles is essential for effective personalization in educational environment. The VARK model (Visual, Auditory, Read/Write, and Kinesthetic) is widely used for this course, yet traditional questionnaire-based assessments struggle with scalability, static data, and limited adaptability. This study introduced an optimized Convolutional Neural Network (CNN) framework for real-time, automated VARK classification using multimodal interaction data. Learner engagement was tracked through event listener technique within a learning management system, capturing HTTP+play/pause for visual and auditory media, HTTP+scroll for reading/writing materials, and HTTP+focus/blur for kinesthetic activities. These event listeners were used to track time spent in each modality and combined with corresponding quiz performance scores to form a comprehensive dataset. The CNN model was trained on twelve thousand (12,000) collected datasets of learners from Hunter e-Academy (He-A) learning management system to classify individual learning styles, enabling dynamic adaptation of content delivery.To evaluate performance, the CNN model was compared through A/B testing against other machine learning (ML) models, including Support Vector Machines (SVM), Random Forest, Naive Bayes, and XGBoost. Metrics such as accuracy, precision, recall, and F1-score were used for assessment. The CNN achieved an accuracy of 99.05%, surpassing SVM (98.01%), XGBoost (98.0%), Random Forest (96.69%), Naive Bayes (96.45%), and Decision Tree (95.98%). It demonstrated perfect precision for Auditory and Read/Write, perfect recall for Visual and Auditory, and F1-scores ?0.98 across all categories, addressing the bias and uneven performance observed in unimodal approaches like KNN (89%). The study confirmed the effectiveness of multimodal data fusion for accurate, objective learning style assessment, offering a scalable, AI-driven alternative to surveys and supporting real-time adaptive learning environments
Development of an Improved Mayfly Algorithm Based Convolutional Neural Network for Pulmonary Diseases Recognition System
Full text linkPulmonary diseases impact the respiratory system. Convolutional Neural Network (CNN) is used for detection and recognition of pulmonary diseases; however, it suffers from hyperparameter selection and overfitting problems. Existing optimization techniques such as the Mayfly Algorithm (MA) also require initial parameter tuning and exhibit slow convergence behaviour. This research developed a Roulette Chaotic Mayfly Algorithm (RCMA) based on CNN (RCMA-CNN) for pulmonary diseases recognition. X-ray images including normal and pulmonary diseases cases were obtained from Kaggle and pre-processed for the desired image quality. The RCMA was formulated using Roulette wheel selection to model attraction deterministically and Chaotic Sinusoidal Map Function to balance exploration and exploitation in the MA. RCMA was applied to optimize CNN hyperparameters including number of layers and batch size at the convolutional layer. This was implemented in MATLAB (R2020a) and compared with MA-CNN and CNN in terms of false positive rate, sensitivity, specificity, accuracy and recognition time. At optimal threshold of 0.75, RCMA-CNN gave false positive rate of 1.43%, sensitivity of 98.06%, specificity of 98.57%, and accuracy of 98.32%. RCMA-CNN recorded a recognition time of 76.81 seconds, which was better than that of MA-CNN and CNN. The RCMA CNN model significantly outperformed both MA-CNN and standard CNN
Urban Justice and State-led Housing Policies for Low-Income Earners in Abuja, Nigeria
Full text linkUrban justice in housing remains a central concern in contemporary urban theory, particularly in African contexts where rapid urbanisation exacerbates socio-spatial inequalities. This paper interrogates how state-led housing policies in Abuja, Nigeria, shape access to affordable housing for low-income earners through the lens of urban justice. Drawing on qualitative and quantitative data, including policy document reviews, stakeholder interviews, and household surveys, the study examines how state interventions mediate the relationship between policy intent and lived experience. Findings indicate that institutional fragmentation, bureaucratic inertia, and market-driven housing approaches undermine equitable access to housing, perpetuating spatial and economic exclusion. Despite policy rhetoric on inclusivity, state- led programmes continue to privilege middle- and upper-income groups. The paper argues for a justice-oriented framework that foregrounds distributive equity, participatory governance, and socially responsive planning in housing delivery. By situating Abuja within global debates on the “just city,” this study contributes to theoretical and empirical understandings of state power, inequality, and justice in African urban contexts
A Framework for Personalized Drug Prescriptions Decision Support System using Hybrid Techniques
Full text linkAbstract The increased complexities in healthcare data require the need to have intelligent systems that can be used to provide accurate as well as a personalized prescription of drugs. This research proposes a novel framework for a Personalized Drug Prescription Decision Support System (PDSS) based on improved approaches that will combine the Viterbi algorithm, neural networks, and Beam Search. The framework will take advantage of the Viterbi algorithm's modeling strength of sequence and follow the most likely course of treatment for a patient's history. As such, to overcome these limitations inherent to a Viterbi algorithm, such as local optimality and a high requirement of memory consumption, a neural network layer will be integrated into dynamically estimated transition and emission probabilities, boosting generalization and the ability to deal with various patient profiles. Additionally, Beam search will be employed to cut the computational overhead and enable exploration of multiple high-probability treatment paths, improving both efficiency and decision robustness. The proposed improved model will use 70% of the data to train and the remaining 30% to test, utilizing the Saliva-Derived SNP Datasets. The main performance indicators will be related to prescription accuracy, the time taken to make a computation, and memory efficiency. A comparison will be made and an analysis performed between the performance of the standard Viterbi algorithm as compared to the enhanced Viterbi algorithm. Early findings will verify the hypothesis that an improved system will be more successful than a traditional single-model system with its ability to apply more precise and resource-saving drug recommendations in accordance with the profiles of individual patients. This framework will present an appealing progress in clinical decision support that advocates a safer and more fruitful delivery of personalized drug prescription
Awareness of Government-provided Solid Waste Management Services in Osun State
Full text linkAwareness of proper solid waste management is a determining factor in residents’ willingness to participate in sustainable waste management practices. This study investigated the residents’ awareness of the government-provided solid waste management (SWM) services in Osun State with a view to raising the implications of effective and efficient SWM. A city in each of the senatorial districts of the state was selected for study. Using multi-stage sampling techniques, 403 household heads were surveyed with 157, 139, and 107 in Osogbo, Ile-Ife, and Ede, respectively. The data collected were analysed using descriptive and inferential statistics. The study showed that many of the respondents had a high level of education, which is a significant factor that influenced their ability to be aware of the SWM service. Most of the respondents in the study area were aware of the SWM services put in place by the government through OWMA, in which Osogbo, which is the state capital, had the highest percentage of respondents who were aware. However, the major source through which the respondents became aware of the OWMA services in Osogbo, Ile-Ife, and Ede was through seeing the solid waste collection vehicles which is not effective enough to educate the residents on the importance of proper solid waste management, the need to put the SWM service provided by OWMA to use and the major roles to play for proper usage. As a result of this, the observed patronage level of the service is low.